Seyedeh Fatemeh Angoshtan
, Zeinab Mori, Saeideh Naeimi
*, Elnaz Mehdizadeh aghdam
*
Abstract
Background: Breast cancer (BC) remains the leading cause of cancer-related mortality among women globally. Despite significant advances in diagnosis and treatment, the molecular mechanisms driving breast tumorigenesis are not yet fully elucidated. This study aimed to identify key genes and signaling pathways associated with BC pathogenesis and prognosis through comprehensive bioinformatic analysis. Method: In this study, gene expression data from the GSE124646 dataset were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified based on the criteria of |log₂ fold change| > 1.5 and p-value < 0.01. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were conducted. In addition, protein–protein interaction (PPI) network was constructed using STRING database and visualized using Cytoscape. Hub genes were identified based on network topology (degree ≥ 7; betweenness centrality between 0.005 and 1). Further validation was performed using the GEPIA web tool and Kaplan–Meier survival analysis. Results: A total of 923 DEGs were identified, comprising 645 upregulated and 278 downregulated genes. Enrichment analysis revealed that these genes were predominantly involved in extracellular matrix (ECM) organization and localized within collagen-containing ECM components. Molecular function analysis indicated significant enrichment in glycosaminoglycan binding. KEGG pathway analysis highlighted the PI3K-Akt signaling pathway as a major pathway implicated in BC. 73 hub genes were identified and incorporated into the PPI network. Survival analysis demonstrated that elevated expression of several hub genes was significantly associated with poor prognosis. GEPIA analysis confirmed aberrant expression of these genes in BC tissues compared to normal controls. Conclusion: These findings enhance our understanding of the molecular underpinnings of breast cancer and highlight potential diagnostic biomarkers and therapeutic targets. Furthermore, this study identifies a subset of previously under-characterized genes, which may contribute to refining the molecular taxonomy and treatment strategies of BC.